Predicting stock market crises using daily stock market valuation and investor sentiment indicators

Fu, Junhui; Zhou, Qingling; Liu, Yufang; Wu, Xiang

doi:10.1016/j.najef.2019.01.002

Cited by 17 publications

(12 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Li et al (2015) shows the significance of S&P 500 futures and options in predicting stock crashes basing on a logit model. By combining the logit model and Ensemble Empirical Mode Decomposition, (Fu et al, 2019) recently develop an EWS for daily stock crashes using investor sentiment indicators and achieve good in-sample and test-set results. Due to the non-linear nature of financial data, machine-learning algorithms are also recognized tools in the general field of stock market prediction.…”

Section: Introductionmentioning

confidence: 99%

“…The CMAX index is the most widely used crisis indicator in the literature concerning stock market early warning(Coudert and Gex, 2008;Li et al, 2015;Fu et al, 2019). It defines stock crashes with an indicator function 1 CM AX t <µ t −λσ t CM AXt : 1, where µt and σt are the mean and standard deviation of CM AXt, and λ is a market-dependent constant(Kaminsky and Reinhart, 1999).…”

mentioning

confidence: 99%

See 1 more Smart Citation

An integrated early warning system for stock market turbulence

Wang

Zong

2020

Expert Systems with Applications

View full text Add to dashboard Cite

This study constructs an integrated early warning system (EWS) that identifies and predicts stock market turbulence. Based on switching ARCH (SWARCH) filtering probabilities of the high volatility regime, the proposed EWS first classifies stock market crises according to an indicator function with thresholds dynamically selected by the two-peak method. An hybrid algorithm is then developed in the framework of a long short-term memory (LSTM) network to make daily predictions that alert turmoils. In the empirical evaluation based on ten-year Chinese stock data, the proposed EWS yields satisfying results with the test-set accuracy of 96.6% and on average 2.4 days of forewarned period. The model's stability and practical value in the real-time decision-making are also proven by the cross-validation and back-testing.

show abstract

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

An integrated early warning system for stock market turbulence

Wang

Zong

2020

Expert Systems with Applications

View full text Add to dashboard Cite

show abstract

“…To measure the sentiments, the common methods include market indicators and direct indicators. Reviewing the relevant literature, both these methods contain defects (Fu et al, 2020). Within the online We-media development in China, sentiment can be derived from online comments with the text analysis technique and machine learning method.…”

Section: B) Public Sentiment and Investor Sentimentmentioning

confidence: 99%

Environmental Regulation, Sentiment, and Eco-Innovation: Evidence From China

Sun

Bao

et al. 2022

Front. Environ. Sci.

View full text Add to dashboard Cite

This study aims to shed light on the effects of environmental regulation on China’s eco-innovation driven by investor sentiment and public sentiment from 2003 to 2017. Adopting a text analysis technique and LSTM model, this study identifies the investor sentiment and public sentiment by online comments. We find that environmental regulation has a positive effect on China’s eco-innovation through public sentiment, whereas environmental regulation inhibits China’s eco-innovation through investor sentiment. Second, the regulatory tools of environmental regulation not only improve the public’s enthusiasm and awareness of environmental protection and eco-innovation in China but also bring the pressure to investor survival and continuous innovation. Third, we confirm that the environmental regulation has threshold effects on general public sentiment and investor sentiment for affecting China’s eco-innovation. Based on this result, environmental regulation easily triggers public sentiment for affecting China’s eco-innovation. Furthermore, in order to test whether environmental regulation has spatial spillover effect, by constructing the Spatial Durbin model, this study finds that the environmental regulation has spatial spillover effects on different regions’ eco-innovation in China through two kinds of sentiment. To the policy implication, this study from two kinds of sentiment to guide the environmental regulators would effectively improve the level of eco-innovation in China.

show abstract

“…They do not include cross-listed firms that trade on different exchanges. Kling and Gao (2008), Chi et al (2012), Han and Li (2017), Fu et al (2020), and Lan et al (2021 are some of the work that concentrates on the Chinese stock market. These studies, however, still pay attention to a single market.…”

Section: Investor Sentiment and Mispricing Of Chinese Stocksmentioning

confidence: 99%

Investor Sentiment, Idiosyncratic Risk, and Stock Price Premium: Evidence From Chinese Cross-Listed Companies

2021

SAGE Open

View full text Add to dashboard Cite

We investigate whether idiosyncratic risk and investor sentiment play important roles in the price disparity between China A-shares and H-shares. To build the investor sentiment indices and decompose them into different fragments for both markets, we use both principal component analysis (PCA) and partial least squares (PLS) approaches. We further look at how idiosyncratic risk affects stock mispricing and how it deals with investor sentiment. We find that the price premium of A-shares over H-shares is strongly linked to the sentiment differential. We also discover that idiosyncratic risk has a major effect on the price premium of cross-listed companies. Moreover, a larger sentiment differential reinforces the impact of idiosyncratic risk on the price disparity. The above results remain robust after controlling for other economic factors.

show abstract

Predicting stock market crises using daily stock market valuation and investor sentiment indicators

Cited by 17 publications

References 42 publications

An integrated early warning system for stock market turbulence

An integrated early warning system for stock market turbulence

Environmental Regulation, Sentiment, and Eco-Innovation: Evidence From China

Investor Sentiment, Idiosyncratic Risk, and Stock Price Premium: Evidence From Chinese Cross-Listed Companies

Contact Info

Product

Resources

About